Advanced Data Analytics for Energy Efficiency

The goal of the project "Advanced Data Analytics for Energy Efficiency" (ADA-EE) is to provide methods and knowledge for automatic recommendation for improvement of energy efficiency in buildings by applying data analytics. The solution offers an automatic evaluation of monitoring data, prediction of future energy needs and prescribing measures to reduce energy usage. By continuously collecting real-time monitoring data, the algorithms can automatically improve prediction accuracy and prescribe better decision options. Methods such as data mining of monitoring data, forecasting and simulations are used to create decision recommendations for optimization of building energy efficiency. The system uses the up-to-date information collected from the property and data stored in the history of the property, together with weather data, as well as semantic data about the building (such as location, function, equipment type) which are analyzed to, for example, detect outliers or identify patterns and trends (descriptive analytics). Patterns are verified, correlations are detected and forecasts for the future are created by extrapolating the patterns (predictive analytics). The results of the previous steps are used to prescribe the optimal actions for improvement (prescriptive analytics). For this, first candidate actions are generated. They are then translated into a set of simulation input parameters. The parameters are used as an input into a baseline simulation, which includes automatic building model generation. The set of simulation results are the predicted potential consequences of the actions. Based on the results, a comparison is performed to recommend the actions with highest value (based on assessment criteria). The suggestions are formalized so that they can be used to generate automatic configuration files. For manual maintenance measures that should be done by maintenance personnel, manuals and human-readable instructions could be automatically generated. By optimizing the energy consumption to the needs of occupants (and if available energy from renewable sources), the solution can maximize the comfort and reduce the costs for energy.